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Ingest X post to pipeline

ingest_x_post_to_pipeline
Destructive

Put one of the operator's already-posted X items into the Media pipeline as the human. Use when the operator posted on X and FO should capture it in Media without a paste. Queues for Keep in Media — does not post to X again. Accepts a tweet id or x.com URL.

Routing: Prefer a tweet_id from list_operator_x_posts rather than typing one from memory

[write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time. Call it on the first clear ask; the card is the yes — do not re-ask in chat.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tweet_idYesTweet id or full x.com status URL.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
pipeline_idNoOptional pipeline to attach (from list_pipelines). Defaults to the company social pipeline.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate destructiveHint=true and readOnlyHint=false, aligning. Description adds useful context: queues for Keep in Media (non-destructive to X), requires approval on first use, and explains approval semantics. It doesn't contradict annotations, and adds value beyond them by explaining the approval workflow and scoping to already-posted items.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is front-loaded with the core action and usage condition, followed by a clear routing hint and approval details. Every sentence provides distinct, useful information without redundancy. Structure is tight and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no output schema, the description covers purpose, usage trigger, routing preference, approval requirements, and side effects (does not repost). No critical information appears missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all params at 100%, but description adds meaning: clarifies tweet_id can be a URL, includes routing tip for tweet_id preference, and notes pipeline_id defaults to company social pipeline. This enhances understanding beyond the schema's basic property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific verb+resource: 'Put ... X items into the Media pipeline'. Clarifies it is for already-posted X items, and distinguishes from posting to X by stating it does not post to X again. Sibling tools like post_to_x and submit_content_to_pipeline are implicitly differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'when the operator posted on X and FO should capture it in Media without a paste'. Provides routing preference: 'Prefer a tweet_id from list_operator_x_posts'. Also notes first-use may require manager approval and how approvals work, giving clear context for when and how to call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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